def add_walking_costs(self):
geod = Geod(ellps="WGS84")
for stop_key in self.stops_label_dict:
stop_dict = self.stops_label_dict[stop_key]
stops_around = Timetable.objects.filter(stop__stopLat__range=(stop_dict["stop_lat"] - LATS_DIFF,
stop_dict["stop_lat"] + LATS_DIFF),
stop__stopLon__range=(stop_dict["stop_lon"] - LON_DIFF,
stop_dict["stop_lon"] + LON_DIFF),
arrivalTime__range=(stop_dict["time_arrival"] - MAX_CHANGE_TIME,
stop_dict["time_arrival"] + MAX_CHANGE_TIME),
date=USER_DATE)
for stop_around in stops_around:
# if change to the same routeId - continue to next loop iteration
if stop_around.route.routeId == stop_dict["route_id"]:
continue
# calculating euclidean distance between 2 stops
lons = [stop_dict["stop_lon"], stop_around.stop.stopLon]
lats = [stop_dict["stop_lat"], stop_around.stop.stopLat]
walk_distance = geod.line_length(lons, lats)
# calculating average time to walk from stop1 to stop
walk_time = walk_distance / AVERAGE_HUMAN_SPEED
# if we can't walk on time to stop_around - continue to next loop iteration
if stop_dict["time_arrival"] + timedelta(minutes=walk_time) > stop_around.arrivalTime:
continue
stop_around_label_key = str(stop_around.route.routeId) + "|" + str(stop_around.stop.stopId)
wait_time_ = -1
# calculate time user has to wait for new transport if stop around is not user endpoint
if stop_around.stop.stopId != self.end_stop_id:
after_walk_datetime = stop_dict["time_arrival"] + timedelta(minutes=walk_time)
wait_time_timedelta = stop_around.arrivalTime - after_walk_datetime
if wait_time_timedelta > MAX_WAIT_TIME:
continue
wait_time_ = CostMatrix.timedelta_to_minutes(wait_time_timedelta)
summed_time = walk_time + wait_time_
else:
summed_time = walk_time
# adding new node to walking_label_dict
if stop_around_label_key not in self.stops_label_dict and stop_around_label_key not in self.walking_label_dict:
self.add_stop_label(stop_around, self.walking_label_dict, stop_around_label_key, walked_in=True)
label_dict = self.walking_label_dict if stop_around_label_key in self.walking_label_dict else self.stops_label_dict
cost_dict_key = str(stop_dict["cost_matrix_label"]) + "+" + \
str(label_dict[stop_around_label_key]["cost_matrix_label"])
# adding new cost to cost_dict
self.cost_dict[cost_dict_key] = [walk_time, wait_time_]
def overground_distance(self, point: (float, float)) -> float:
"""
horizontal distance over ellipsoid
:param point: (x, y) point
:return: distance in ellipsodal units
"""
if not isinstance(point, numpy.ndarray):
point = numpy.array(point)
coordinates = numpy.stack([self.coordinates[:2], point], axis=0)
if self.crs.is_projected:
return numpy.hypot(*numpy.sum(numpy.diff(coordinates, axis=0), axis=0))
else:
ellipsoid = self.crs.datum.to_json_dict()["ellipsoid"]
geodetic = Geod(
a=ellipsoid["semi_major_axis"], rf=ellipsoid["inverse_flattening"]
)
return geodetic.line_length(coordinates[:, 0], coordinates[:, 1])
def get_linelength_between_points(C1: list, C2: list) -> float:
geod = Geod(ellps="WGS84")
total_length = geod.line_length(C1, C2)
return total_length
def test_line_length__radians():
geod = Geod(ellps="WGS84")
total_length = geod.line_length([1, 2], [0.5, 1], radians=True)
assert_almost_equal(total_length, 5426061.32197463, decimal=3)
def test_line_length__single_point():
geod = Geod(ellps="WGS84")
assert geod.line_length(1, 1) == 0
def run_skill_analysis(drifter_file,
drifter_id,
skill_files,
date_range,
grid,
period=pd.Timedelta('3 Days'),
data_freq=pd.Timedelta('60 minutes')):
"""Calculate the skill score for a given particle run. Here, the skill score
is calculated as in Liu and Weisberg (2011).
Keyword arguments:
drifter_file -- a .csv file of the drifter data with the columns 'datetime',
'lat', and 'lon'
skill_files -- a sorted list of the model output netCDF files that contain
the tracks from the model runs for skill
date_range -- a numpy.ndarray object of numpy.datetime64 objects, the
drifter and output data frequencies should match
period -- a pandas Timedelta object representing the time period for which
to calculate the skill
data_freq -- a pandas Timedelta object representing the frequency of the
drifter data
Returns:
a numpy array of particle times, trajectory lengths, separation distances,
and skill scores
"""
geod = Geod(ellps="WGS84")
drifter_data = get_drifter_data(drifter_file, drifter_id)
drifter_data = drifter_data[drifter_data['datetime'].isin(date_range)]
# query one of the files to find the number of particles
rootgrp = Dataset(skill_files[0])
num_particles = rootgrp['release'].size
skill_data = np.empty((len(skill_files), num_particles, 4), dtype='O')
# for each skill output file
for i in range(0, len(skill_files)):
# open model data and convert times to datetimes
rootgrp = Dataset(skill_files[i])
units, epoch = rootgrp['time'].units.split(' since ')
times = rootgrp['time'][:]
times = pd.Timestamp(epoch) + pd.TimedeltaIndex(times, unit=units)
# get the drifter coordinates at corresponding times as model output
drift_indices = drifter_data['datetime'].isin(times)
drifter_lats = drifter_data['lat'].values[drift_indices]
drifter_lons = drifter_data['lon'].values[drift_indices]
# get the model coordinates
model_indices = times.isin(drifter_data['datetime'])
model_lats = rootgrp['lat'][:, model_indices]
model_lons = rootgrp['lon'][:, model_indices]
# for each particle
for j in range(0, num_particles):
# find the total length of the trajectory by adding each path length
traj_len = geod.line_length(model_lons[j], model_lats[j])
# find the separation distance
lons = np.array([drifter_lons[-1], model_lons[j, -1]])
lats = np.array([drifter_lats[-1], model_lats[j, -1]])
sep_distance = geod.line_length(lons, lats)
c = sep_distance / traj_len
s = np.maximum(0, 1 - c)
# save the skill information to array
skill_data[i, j] = np.array([times[0], traj_len, sep_distance, s])
return skill_data
def aco_algorithm(num_iteration, ants, nodes, visibility, cost_matrix_object, e, alpha, beta, display_num):
geod = Geod(ellps="WGS84")
cost_matrix = cost_matrix_object.cost_matrix
stops_label_dict = cost_matrix_object.stops_label_dict
walking_label_dict = cost_matrix_object.walking_label_dict
start_nodes = cost_matrix_object.start_nodes
end_nodes = cost_matrix_object.end_nodes
route_dict = {}
best_route = []
dist_min_cost = 0
dist_min_cost_arr = []
if not start_nodes or not end_nodes:
return route_dict, best_route, dist_min_cost, dist_min_cost_arr
# FIXME consider multiple starting nodes
# now we are taking into consideration only first possible start point
start = stops_label_dict[start_nodes[0]]["cost_matrix_label"]
possible_ends = [stops_label_dict[possible_end]["cost_matrix_label"] for possible_end in end_nodes if possible_end in stops_label_dict]
possible_ends.extend([walking_label_dict[possible_end]["cost_matrix_label"] for possible_end in end_nodes if possible_end in walking_label_dict])
# initializing pheromone present at the paths to stops
pheromone = .1 * np.ones((nodes, nodes))
branches_deleted = 0
for ite in range(num_iteration):
# plots pheromone levels
display_pheromone(pheromone, num_iteration, ite + 1, display_num, cost_matrix_object.cost_matrix_size)
for i in range(ants):
# initial starting position of every ant
route = [start]
temp_visibility = np.array(visibility) # creating a copy of visibility
node = route[0]
while node not in possible_ends:
cur_loc = node
temp_visibility[:, cur_loc] = 0 # making visibility of the current node equals zero
p_feature = np.power(pheromone[cur_loc, :], beta) # calculating pheromone feature
v_feature = np.power(visibility[cur_loc, :], alpha) # calculating visibility feature
p_feature = p_feature[:, np.newaxis] # adding axis to make a size[5,1]
v_feature = v_feature[:, np.newaxis] # adding axis to make a size[5,1]
combine_feature = np.multiply(p_feature, v_feature) # calculating the combine feature
# checking if ant can go any further - if not - ant come back to start node and
# tries to search for food again
if not np.any(combine_feature):
# adding walk path to first endnode
last_stop_key_label = cost_matrix_object.find_label_dict_key_with_cost_matrix_label(node)
endpoint_key_label = end_nodes[0]
last_stop_label_dict = walking_label_dict if last_stop_key_label in walking_label_dict else stops_label_dict
endpoint_label_dict = walking_label_dict if endpoint_key_label in walking_label_dict else stops_label_dict
# calculating euclidean distance between 2 stops
lons = [last_stop_label_dict[last_stop_key_label]["stop_lon"], endpoint_label_dict[endpoint_key_label]["stop_lon"]]
lats = [last_stop_label_dict[last_stop_key_label]["stop_lat"], endpoint_label_dict[endpoint_key_label]["stop_lat"]]
walk_distance = geod.line_length(lons, lats)
# calculating average time to walk from stop1 to stop
walk_time = walk_distance / AVERAGE_HUMAN_SPEED
if walk_time > 30:
# TODO - for every node - calculate walk time to check if we are not close enough to endpoint
counter = len(route) - 2
for route_node in reversed(route[1:]):
# delete non optimal graph branch
node_costs = cost_matrix_object.return_cost_matrix_cost_for_row(route_node)
# if node does not have any other branches - delete it
non_zero_cols_indexes = np.nonzero(node_costs)[0] # returns list
if len(non_zero_cols_indexes) == 0:
cost_matrix_object.cost_matrix[route[counter]][route_node] = [0, -1]
cost_matrix[route[counter]][route_node] = [0, -1]
branches_deleted += 1
visibility[route[counter]][route_node] = 0
temp_visibility[route[counter]][route_node] = 0
print(f"node {route[counter]}->{route_node} deleted, branches deleted: {branches_deleted}")
else:
break
counter -= 1
route = [start]
node = route[0]
continue
else:
cost_matrix[node][possible_ends[0]] = [walk_time, 0]
route.append(possible_ends[0])
break
total = np.sum(combine_feature) # sum of all the feature
# finding probability of element probabilities(i) = combine_feature(i)/total
probabilities = combine_feature / total
# if not node:
# print(f"probabils: {probabilities}")
cumulative_probabilities = np.cumsum(probabilities) # calculating cumulative sum
r = np.random.random_sample() # random number in [0,1)
# finding the next node having probability higher then random number (r)
node = np.nonzero(cumulative_probabilities > r)[0][0]
route.append(node)
route_dict[i] = route
dist_cost = {}
for key in route_dict:
route_cost = []
for counter, node in enumerate(route_dict[key][:-1]):
# calculating total tour distance
cost = cost_matrix_object.return_cost_matrix_cost(int(node), int(route_dict[key][counter+1]))
route_cost.append(cost)
dist_cost[key] = route_cost # storing distance of tour for 'i'th ant at location 'i'
dist_cost_sum = {key: sum(route_cost) for key, route_cost in dist_cost.items()}
dist_min_loc = min(dist_cost_sum, key=dist_cost_sum.get) # finding location of minimum of dist_cost
dist_min_cost = dist_cost_sum[dist_min_loc] # finding min of dist_cost
best_route = route_dict[dist_min_loc] # initializing current traversed as best route
pheromone = (1 - e) * pheromone # evaporation of pheromone with (1-e)
for key in route_dict:
for counter, node in enumerate(route_dict[key][:-1]):
dt = 1 / dist_cost_sum[key]
pheromone[int(node), int(route_dict[key][counter+1])] += dt
# updating the pheromone with delta distance (dt)
# dt will be greater when distance will be smaller
return route_dict, best_route, dist_min_cost
from pyproj import CRS
from pyproj import Geod
print("doing pyproj test")
crs = CRS.from_epsg(4326)
assert crs.to_authority() == ('EPSG', '4326')
assert crs.prime_meridian.name == "Greenwich"
# and test based on geodesic line length example from
# https://pyproj4.github.io/pyproj/stable/examples.html
lats = [
-72.9, -71.9, -74.9, -74.3, -77.5, -77.4, -71.7, -65.9, -65.7, -66.6,
-66.9, -69.8, -70.0, -71.0, -77.3, -77.9, -74.7
]
lons = [
-74, -102, -102, -131, -163, 163, 172, 140, 113, 88, 59, 25, -4, -14, -33,
-46, -61
]
geod = Geod(ellps="WGS84")
total_length = geod.line_length(lons, lats)
assert 14 < total_length / 1e6 < 15
sites = ['OPO','MAU','WEST','FLE','TAS','LWR','CAP','CAM','KAI','GOB','TIM','HSB','BGB','FIO']
site = sites[int(sys.argv[1])]
geod = Geod(ellps="WGS84")
for file in sorted(glob.glob(f'/nesi/nobackup/vuw03073/bigboy/all_settlement/{site}*')):
traj = nc.Dataset(file)
ym = file[-9:-3]
print(f'{site}_{ym}', flush=True)
lon = traj.variables['lon'][:]
lat = traj.variables['lat'][:]
dists = np.zeros((len(lon), 2))
for part in range(len(lon)):
part_lons = lon[part][np.where(lon[part].mask==False)]
part_lats = lat[part][np.where(lat[part].mask==False)]
along_track = geod.line_length(part_lons, part_lats)
start_finish = geod.line_length([part_lons[0], part_lons[-1]], [part_lats[0],part_lats[-1]])
dists[part, 0] = along_track
dists[part, 1] = start_finish
outFile = open(f'bigboy_distances/{site}_{ym}.txt', 'w')
np.savetxt(outFile, dists)
outFile.close()
# def calc_along_track(trajectory):
# import pdb; pdb.set_trace()
# lons = trajectory.lon.data
# lats = trajectory.lat.data
# lons = lons[(lons>-10000) & (lons < 10000)]
# lats = lats[(lats>-10000) & (lats < 10000)]
# along_track = 0
# for i in range(len(lons)-1):